Sensors

33 pages, 7833 KB

Open AccessArticle

Motion Artifacts Removal from Measured Arterial Pulse Signals at Rest: A Generalized SDOF-Model-Based Time–Frequency Method

by Zhili Hao

Sensors 2025, 25(21), 6808; https://doi.org/10.3390/s25216808 - 6 Nov 2025

Viewed by 532

Abstract

Motion artifacts (MA) are a key factor affecting the accuracy of a measured arterial pulse signal at rest. This paper presents a generalized time–frequency method for MA removal that is built upon a single-degree-of-freedom (SDOF) model of MA, where MA is manifested as [...] Read more.

Motion artifacts (MA) are a key factor affecting the accuracy of a measured arterial pulse signal at rest. This paper presents a generalized time–frequency method for MA removal that is built upon a single-degree-of-freedom (SDOF) model of MA, where MA is manifested as time-varying system parameters (TVSPs) of the SDOF system for the tissue–contact-sensor (TCS) stack between an artery and a sensor. This model distinguishes the effects of MA and respiration on the instant parameters of harmonics in a measured pulse signal. Accordingly, a generalized SDOF-model-based time–frequency (SDOF-TF) method is developed to obtain the instant parameters of each harmonic in a measured pulse signal. These instant parameters are utilized to reconstruct the pulse signal with MA removal and extract heart rate (HR) and respiration parameters. The method is applied to analyze seven measured pulse signals at rest under different physiological conditions using a tactile sensor and a PPG sensor. Some observed differences between these conditions are validated with the related findings in the literature. As compared to instant frequency, the instant initial phase of a harmonic extracts respiration parameters with better accuracy. Since HR variability (HRV) affects arterial pulse waveform (APW), the extracted APW with a constant HR serves better for deriving arterial indices. Full article

(This article belongs to the Special Issue Advances in Biosignal Sensing and Signal Processing)

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16 pages, 2853 KB

Open AccessArticle

Sensitivity Improvement of MEMS Resonant Accelerometers by Shape Optimization of Microlevers and Resonators

by Longqi Ran, Wensheng Zhao, Ting Li, Jiangbo He and Wu Zhou

Sensors 2025, 25(21), 6807; https://doi.org/10.3390/s25216807 - 6 Nov 2025

Viewed by 483

Abstract

High-frequency sensitivity to external acceleration is crucial for improving the accuracy of MEMS resonant accelerometers. This study proposes utilizing shape optimization of microlevers and resonators to improve sensitivity. Initially, an optimization model for microlevers is established, considering the arm’s shape and the dimensions [...] Read more.

High-frequency sensitivity to external acceleration is crucial for improving the accuracy of MEMS resonant accelerometers. This study proposes utilizing shape optimization of microlevers and resonators to improve sensitivity. Initially, an optimization model for microlevers is established, considering the arm’s shape and the dimensions of the pivots, outputs, inputs, and supported beams. Secondly, shape optimization for the resonant beam of the tuning fork resonators is implemented, utilizing a bi-objective function to maintain the fundamental frequency. Finally, the genetic algorithm is employed in both optimizations to search for the global optimal solution. The microlever optimization achieves a high sensitivity of 286.9 Hz/g, and the final trapezoidal arm shape offers the advantage of accommodating a larger proof mass within a given die area. Meanwhile, the resonator optimization improves the sensitivity to axial inertial force from 727 Hz/mN to 1338.5 Hz/mN while keeping the fundamental frequency at approximately 20,000 Hz. Integrating the optimized microlevers and resonators yields a very high sensitivity of 480.2 Hz/g, and the sensitivity per proof mass area is significantly higher than that reported in previous studies. Full article

(This article belongs to the Section Sensors Development)

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15 pages, 2443 KB

Open AccessArticle

A Switched-Line True Time Delay Unit for Wideband Phased Arrays Using Packaged RF MEMS Switches

by David W. K. Thomas, Kai Wu and Y. Jay Guo

Sensors 2025, 25(21), 6806; https://doi.org/10.3390/s25216806 - 6 Nov 2025

Viewed by 497

Abstract

The growing demand for wideband electronically scanned arrays (ESAs) in next-generation radar, satellite, and 5G/6G systems has renewed interest in true time delay units (TDUs) to overcome the limitations of phase-based beamforming. In parallel, recent advances in the commercial availability and reliability of [...] Read more.

The growing demand for wideband electronically scanned arrays (ESAs) in next-generation radar, satellite, and 5G/6G systems has renewed interest in true time delay units (TDUs) to overcome the limitations of phase-based beamforming. In parallel, recent advances in the commercial availability and reliability of packaged RF MEMS switches have enabled practical hardware implementations once considered infeasible. This paper presents the design, fabrication, and experimental validation of a broadband, 4-bit switched-line TDU using only off-the-shelf components and standard PCB processes. The unit operates from 0.4 to 6 GHz, with a total delay range of 0–413 ps, achieving an average insertion loss of 1.5 dB and delay error below 18.4 ps, resulting in a figure of merit (FOM) of 152.8 ps/dB. Measured results are reported alongside a refined switch/termination model that aligns simulations with measurements. This is among the first reported demonstrations of a complete RF MEMS-based TDU implemented entirely with commercially available components in a standard PCB-integrated implementation. These results demonstrate a practical pathway toward scalable MEMS-based TDUs for deployment in advanced beamforming systems. Full article

(This article belongs to the Section Communications)

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17 pages, 15426 KB

Open AccessArticle

LiDAR-Based Long-Term Mapping in Snow-Covered Environments

by Jaewon Lee, Woojin Chung and Jiwoong Kim

Sensors 2025, 25(21), 6805; https://doi.org/10.3390/s25216805 - 6 Nov 2025

Viewed by 591

Abstract

Autonomous driving systems encounter various uncertainties in real-world environments, many of which are difficult to represent in maps. Among them, accumulated snow poses a unique challenge since its shape and volume gradually change over time. If accumulated snow is included in a map, [...] Read more.

Autonomous driving systems encounter various uncertainties in real-world environments, many of which are difficult to represent in maps. Among them, accumulated snow poses a unique challenge since its shape and volume gradually change over time. If accumulated snow is included in a map, it leads to two main problems. First, during long-term driving, discrepancies between the actual and mapped environments, caused by melting snow, can significantly degrade localization performance. Second, the inclusion of large amounts of accumulated snow in the map can cause registration errors between sessions, thereby hindering accurate map updates. To address these issues, we propose a mapping strategy specifically designed for snow-covered environments. The proposed method first detects and removes accumulated snow using a deep learning-based approach. The resulting snow-free data are then used for map updating, and the ground information occluded by snow is subsequently restored. The effectiveness of the proposed method is validated with data collected in real-world snow-covered environments. Experimental results demonstrate that the proposed method achieves 78.6% IoU for snow detection and reduces map alignment errors by 12.5% (RMSE) and 15.6% (Chamfer Distance) on average, contributing to maintaining map quality and enabling long-term autonomous driving in snow-covered environments. Full article

(This article belongs to the Section Environmental Sensing)

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20 pages, 5313 KB

Open AccessArticle

Feasibility of Initial Bias Estimation in Real Maritime IMU Data Including X- and Y-Axis Accelerometers

by Gen Fukuda and Nobuaki Kubo

Sensors 2025, 25(21), 6804; https://doi.org/10.3390/s25216804 - 6 Nov 2025

Viewed by 378

Abstract

This study aimed to validate a bias estimation framework for low-cost maritime IMUs by applying it to real-world shipborne data. Six estimation methods—including statistical (mean, median), model-based (least squares, cross-correlation), and signal-processing approaches (FFT, Butterworth filter)—were compared. The results demonstrated that the low-frequency [...] Read more.

This study aimed to validate a bias estimation framework for low-cost maritime IMUs by applying it to real-world shipborne data. Six estimation methods—including statistical (mean, median), model-based (least squares, cross-correlation), and signal-processing approaches (FFT, Butterworth filter)—were compared. The results demonstrated that the low-frequency Butterworth filter achieved the smallest residuals, with RMS residuals below 0.038 m/s² for accelerometers and 0.0035 deg/s for gyroscopes. In particular, AccX and AccZ residuals converged to 3.04 × 10⁻² m/s² and 2.30 × 10⁻² m/s², respectively, while GyroZ achieved 5.58 × 10⁻⁴ deg/s. Estimated accelerometer biases were 0.0405 m/s² (X-axis) and 0.1615 m/s² (Y-axis), and the optimization successfully converged with an objective function value of 9.314. The findings confirm that the previously proposed bias estimation method, originally validated in simulation, is effective under real-world maritime conditions. However, as ground truth bias values cannot be obtained in shipborne experiments, verification relied on residual statistics and cross-correlation analysis. This limitation has been explicitly stated in the conclusion, and future studies should incorporate sensitivity analyses and controlled experiments to further quantify error sources. Full article

(This article belongs to the Collection Position Sensor)

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27 pages, 2847 KB

Open AccessArticle

Hierarchical Beamforming Optimization for ISAC-Enabled RSU Systems in Complex Urban Environments

by Zhiyuan You, Na Lv, Guimei Zheng and Xiang Wang

Sensors 2025, 25(21), 6803; https://doi.org/10.3390/s25216803 - 6 Nov 2025

Viewed by 403

Abstract

Integrated Sensing and Communication (ISAC)-enabled Roadside Units (RSUs) encounter significant performance trade-offs between target sensing and multi-user communication in complex urban environments, where conventional optimization methods are prone to converging to local optima and joint optimization methods often yield sub-optimal results due to [...] Read more.

Integrated Sensing and Communication (ISAC)-enabled Roadside Units (RSUs) encounter significant performance trade-offs between target sensing and multi-user communication in complex urban environments, where conventional optimization methods are prone to converging to local optima and joint optimization methods often yield sub-optimal results due to conflicting objectives. To address the challenge of trade-off between sensing and communication performance, this paper proposes a hierarchical beamforming optimization solution designed to tackle joint sensing–communication problems in such scenarios. The overall optimization problem is decomposed into a two-level “leader-follower” structure. In the leader layer, we introduce a max–min strategy based on the bisection method to transform the non-convex Signal-to-Interference-plus-Noise Ratio (SINR) optimization problem into a second-order cone constraint problem and solve the communication beamforming vector. In the follower layer, the Signal-to-Clutter-plus-Noise Ratio (SCNR) maximization problem is converted into a Semi-Definite Programming (SDP) problem solved via the CVX toolbox. Additionally, we introduce a “spatiotemporal resource isolation” mechanism to project the sensing beam onto the null space of the communication channel. The hierarchical optimization solution jointly optimizes communication SINR and sensing SCNR, enabling an effective balance between sensing accuracy and communication reliability. Simulation results demonstrate the proposed method’s effectiveness in simultaneously improving sensing accuracy and communication reliability. Full article

(This article belongs to the Special Issue Integrated Sensing and Communication in IoT Applications)

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22 pages, 1596 KB

Open AccessArticle

A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks

by Ge Zhang, Weimin Shi, Qilong Miao and Xiaofeng Shen

Sensors 2025, 25(21), 6802; https://doi.org/10.3390/s25216802 - 6 Nov 2025

Viewed by 393

Abstract

The precise reconstruction of target scattering centers (TSCs) using sensors plays a crucial role in feature extraction and identification of non-cooperative targets. Radar sensor networks (RSNs) are well suited for this task, as they are capable of illuminating targets from multiple aspect angles [...] Read more.

The precise reconstruction of target scattering centers (TSCs) using sensors plays a crucial role in feature extraction and identification of non-cooperative targets. Radar sensor networks (RSNs) are well suited for this task, as they are capable of illuminating targets from multiple aspect angles and rapidly capturing reflected signals. However, the complex geometry and diverse material composition of real-world targets result in significant variations in the radar cross-section (RCS) observed at different angles. Although these RCS responses are interrelated, they exhibit considerable angular diversity. Furthermore, achieving precise spatiotemporal registration and fully coherent processing is infeasible for RSNs composed of small mobile sensor platforms, such as drone swarms. Therefore, an intelligent algorithm is required to extract and accumulate correlated and meaningful information from the target echoes received by the RSN. In this work, a novel collaborative TSC reconstruction framework for RSNs is proposed. The framework performs similarity evaluation on wide-angle high-resolution range profiles (HRRPs) to achieve adaptive angular segmentation of TSC models. It combines the expectation–maximization (EM) algorithm with an enhanced Arctic puffin optimization (EAPO) algorithm to effectively integrate echo information from the RSN in a non-coherent manner, thereby enabling accurate TSC estimation. The proposed method outperforms existing mainstream approaches in terms of spatiotemporal registration requirements, estimation accuracy, and stability. Comparative experiments on measured datasets demonstrate the robustness of the framework and its adaptability to complex target scattering characteristics, confirming its practical value. Full article

(This article belongs to the Special Issue Computer Vision Recognition and Communication Sensing System)

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17 pages, 552 KB

Open AccessArticle

Enhancing the Reliability of AD936x-Based SDRs for Aerospace Applications via Active Register Scrubbing and Autonomous Fault Recovery

by Jinyang Wang, Zhugang Wang and Li Zhou

Sensors 2025, 25(21), 6801; https://doi.org/10.3390/s25216801 - 6 Nov 2025

Viewed by 424

Abstract

Single Event Upsets (SEUs) in Commercial Off-The-Shelf (COTS) Software-Defined Radios (SDRs) are frequent in a erospace applications, especially in GEO (Geostationary Orbit) orbit during severe solar activity, and can lead to unexpected register corruption and communication failures. This work presents a purely software-based [...] Read more.

Single Event Upsets (SEUs) in Commercial Off-The-Shelf (COTS) Software-Defined Radios (SDRs) are frequent in a erospace applications, especially in GEO (Geostationary Orbit) orbit during severe solar activity, and can lead to unexpected register corruption and communication failures. This work presents a purely software-based Fault Detection, Isolation, and Recovery (FDIR) framework tailored for the AD936x RF agile transceiver, requiring no hardware modifications. The proposed method classifies all device registers into four impact categories and applies dedicated scrubbing strategies—standard refresh, masked refresh, procedural refresh, and forced refresh—combined with real-time register health monitoring and adaptive recovery actions. Fault injection experiments comprising 10,000 diverse test cases achieved 100% fault coverage for the tested scenarios, with an average recovery time of 0.75 s for typical SEUs and a guaranteed worst-case recovery under 4.4 s for critical failures, while maintaining a CPU load below 1.3%. The approach ensures continuous SDR operation under SEU events and offers a scalable, lightweight, and cost-effective reliability enhancement for CubeSats and other resource-constrained aerospace platforms. Full article

(This article belongs to the Section Communications)

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27 pages, 1112 KB

Open AccessArticle

Joint Coherent/Non-Coherent Detection for Distributed Massive MIMO: Enabling Cooperation Under Mixed Channel State Information

by Supuni Gunasekara, Peter Smith, Margreta Kuijper and Rajitha Senanayake

Sensors 2025, 25(21), 6800; https://doi.org/10.3390/s25216800 - 6 Nov 2025

Viewed by 557

Abstract

Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) [...] Read more.

Beyond-5G wireless systems increasingly rely on distributed massive multiple-input multiple-output (MIMO) architectures to achieve high spectral efficiency, low latency, and wide coverage. A key challenge in such networks is that cooperating base stations (BSs) often possess different levels of channel state information (CSI) due to fronthaul constraints, user mobility, or hardware limitation. In this paper, we propose two novel detectors that enable cooperation between BSs with differing CSI availability. In this setup, some BSs have access to instantaneous CSI, while others only have long-term channel information. The proposed detectors—termed the coherent/non-coherent (CNC) detector and the differential CNC detector—integrate coherent and non-coherent approaches to signal detection. This framework allows BSs with only long-term information to actively contribute to the detection process, while leveraging instantaneous CSI where available. This approach enables the system to integrate the advantages of non-coherent detection with the precision of coherent processing, improving overall performance without requiring full CSI at all cooperating BSs. We formulate the detectors based on the maximum likelihood (ML) criterion and derive analytical expressions for their pairwise block error probabilities under Rayleigh fading channels. Leveraging the pairwise block error probability expression for the CNC detector, we derive a tight upper bound on the average block error probability. Numerical results show that the CNC and differential CNC detectors outperform their respective single-BS baseline-coherent ML and non-coherent differential detection. Moreover, both detectors demonstrate strong resilience to mid-to-high range correlation at the BS antennas. Full article

(This article belongs to the Special Issue Future Wireless Communication Networks: 3rd Edition)

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25 pages, 3393 KB

Open AccessArticle

Enhancing Driver Monitoring Systems Based on Novel Multi-Task Fusion Algorithm

by Romas Vijeikis, Ibidapo Dare Dada, Adebayo A. Abayomi-Alli and Vidas Raudonis

Sensors 2025, 25(21), 6799; https://doi.org/10.3390/s25216799 - 6 Nov 2025

Viewed by 732

Abstract

Distracted driving continues to be a major contributor to road accidents, highlighting the growing research interest in advanced driver monitoring systems for enhanced safety. This paper seeks to improve the overall performance and effectiveness of such systems by highlighting the importance of recognizing [...] Read more.

Distracted driving continues to be a major contributor to road accidents, highlighting the growing research interest in advanced driver monitoring systems for enhanced safety. This paper seeks to improve the overall performance and effectiveness of such systems by highlighting the importance of recognizing the driver’s activity. This paper introduces a novel methodology for assessing driver attention by using multi-perspective information using videos that capture the full driver body, hands, and face and focusing on three driver tasks: distracted actions, gaze direction, and hands-on-wheel monitoring. The experimental evaluation was conducted in two phases: first, assessing driver distracted activities, gaze direction, and hands-on-wheel using a CNN-based model and videos from three cameras that were placed inside the vehicle, and second, evaluating the multi-task fusion algorithm, considering the aggregated danger score, which was introduced in this paper, as a representation of the driver’s attentiveness based on the multi-task data fusion algorithm. The proposed methodology was built and evaluated using a DMD dataset; additionally, model robustness was tested on the AUC_V2 and SAMDD driver distraction datasets. The proposed algorithm effectively combines multi-task information from different perspectives and evaluates the attention level of the driver. Full article

(This article belongs to the Special Issue Computer Vision-Based Human Activity Recognition)

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10 pages, 521 KB

Open AccessReview

Critical Narrative Review of the Applications of Near-Infrared Spectroscopy Technology in Sports Science

by Carlos Sendra-Pérez, Alberto Encarnación-Martínez and Jose I. Priego-Quesada

Sensors 2025, 25(21), 6798; https://doi.org/10.3390/s25216798 - 6 Nov 2025

Viewed by 865

Abstract

Near-Infrared Spectroscopy (NIRS) is a noninvasive technology used to monitor muscle oxygenation in sports science. Since its introduction in 1977, NIRS has evolved into a valuable tool for assessing physiological responses during exercise and rehabilitation. The history of NIRS dates back to early [...] Read more.

Near-Infrared Spectroscopy (NIRS) is a noninvasive technology used to monitor muscle oxygenation in sports science. Since its introduction in 1977, NIRS has evolved into a valuable tool for assessing physiological responses during exercise and rehabilitation. The history of NIRS dates back to early hemoglobin studies in the 19th century, with significant advancements in pulse oximetry during World War II. By the late 1980s, NIRS had become widely used in sports science, allowing researchers to evaluate muscle perfusion and metabolic thresholds in various activities. NIRS applications in sports include determining exercise thresholds, monitoring muscle oxygenation during training, assessing asymmetries between limbs, and evaluating mitochondrial capacity. Studies have explored its use in both team and endurance sports, highlighting its role in optimizing performance and preventing injuries. Beyond sports, NIRS technology is expanding into clinical fields, aiding in rehabilitation and patient monitoring. This critical review has identified several key areas for future research, including the need to clarify methodological influences, strategies to minimize the impact of adipose tissue on NIRS measurements, the importance of conducting longitudinal studies, increased research on sex-specific effects, and a greater emphasis on field-based studies. With continued advancements, NIRS is expected to further enhance our understanding of muscle physiology and human performance, making it a crucial tool in athletic performance assessment and clinical practice. Full article

(This article belongs to the Special Issue Technologies Related to Oxygen Supply to Tissue: Measurements of Oxygen Saturation, Tissue Blood Flow and Arterial Blood Pressure)

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12 pages, 506 KB

Open AccessArticle

Adaptive Channel Estimation for Semi-Passive IRS with Optimized Sensor Deployment

by Zhiyu Han, Hanning Wang, Yafeng Wang and Zhuo Fan

Sensors 2025, 25(21), 6797; https://doi.org/10.3390/s25216797 - 6 Nov 2025

Viewed by 389

Abstract

To achieve optimal passive beamforming gains from Intelligent Reflective Surfaces (IRS), accurate Channel State Information (CSI) acquisition is required. However, the IRS, with numerous passive devices, lacks the ability to process signals, resulting in considerable challenges in obtaining accurate CSI. Based on the [...] Read more.

To achieve optimal passive beamforming gains from Intelligent Reflective Surfaces (IRS), accurate Channel State Information (CSI) acquisition is required. However, the IRS, with numerous passive devices, lacks the ability to process signals, resulting in considerable challenges in obtaining accurate CSI. Based on the semi-passive IRS, this paper proposes a compressed sensing channel estimation algorithm without knowing the path number of channel, which improves the accuracy of channel estimation. Furthermore, a particle swarm optimization (PSO)-based deployment scheme for active sensors in the semi-passive IRS is developed. Numerical simulations confirm the effectiveness, demonstrating a reduction in Normalized Mean Square Error (NMSE) and improved channel estimation with fewer pilot symbols, thereby minimizing estimation overhead. Full article

(This article belongs to the Special Issue Advanced Massive MIMO Antenna Arrays, Metasurfaces and Reconfigurable Intelligent Surfaces for Sensing, Localization, and Wireless Communications: 2nd Edition)

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23 pages, 4560 KB

Open AccessArticle

Deep Learning Image-Based Fusion Approach for Identifying Multiple Apparent Diseases in Concrete Structure

by Yongsheng Tang, Yaomin Wei, Lengfeng Qian and Long Liu

Sensors 2025, 25(21), 6796; https://doi.org/10.3390/s25216796 - 6 Nov 2025

Viewed by 407

Abstract

Addressing the key pain points in detecting typical apparent diseases of concrete structures, where standalone object detection fails to achieve pixel-level quantification and standalone semantic segmentation, is inefficient. Therefore, a deep learning image-based fusion approach is proposed to identify the typical visible diseases [...] Read more.

Addressing the key pain points in detecting typical apparent diseases of concrete structures, where standalone object detection fails to achieve pixel-level quantification and standalone semantic segmentation, is inefficient. Therefore, a deep learning image-based fusion approach is proposed to identify the typical visible diseases in concrete structures, namely crack, spalling, water leakage, and seam deformation. To implement the approach, a deep learning fusion network is developed with the YOLO and UNet models to identify multiple apparent diseases rapidly. In the fusion network, the YOLO model is used to filter the images containing the visible diseases from all the images in the first stage. Then, the UNet model is used to extract the pixels containing diseases from the selected images. Lastly, analysis methods are proposed to quantify the diseases based on the segmented pixels, such as length, width, and area. In this paper, a dataset of 1488 images with the above diseases from a field inspection was used to train the deep learning fusion network. The training results demonstrated the robustness of the fusion network in identifying and segmenting diseases with a mean average precision of 0.72 and a Dice score of 0.82. Experiments were finally conducted on concrete slabs with simulated diseases for additional validation. The results indicated that the proposed fusion network could identify the diseases approximately 50% faster than the UNet model only. The quantification precision was found to be satisfactory, with relative errors below 11.07% for the area of water leakage, below 5% for the length and area of cracks, and below 6% for the width of seams. Full article

(This article belongs to the Special Issue Advanced Sensing Systems for Structural Monitoring and Damage Identification of Buildings and Bridges)

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18 pages, 1512 KB

Open AccessArticle

Enhancing Quality of Resident Care and Staff Efficiency Through Implementation of Sensors in the Long-Term Care Setting: A Multi-Site Mixed-Methods Study

by Shannon Freeman, Santiago Otalvaro Zapata and Matthew J. Sargent

Sensors 2025, 25(21), 6795; https://doi.org/10.3390/s25216795 - 6 Nov 2025

Viewed by 610

Abstract

Introduction: Individuals residing in long-term care facilities (LTCFs) often experience poor sleep quality. Emerging sensor technologies may improve resident sleep quality and reduce staff workload. This evaluation assessed the impact of a bed sensor technology on LTCF staff experiences and resident outcomes. Methods: [...] Read more.

Introduction: Individuals residing in long-term care facilities (LTCFs) often experience poor sleep quality. Emerging sensor technologies may improve resident sleep quality and reduce staff workload. This evaluation assessed the impact of a bed sensor technology on LTCF staff experiences and resident outcomes. Methods: A mixed-methods evaluation examined the impact of a pilot implementation of Toch Sleepsense, a non-wearable sensor placed under residents’ beds, which monitors sleep patterns, movement, and vital signs. Data were gathered from staff surveys, interviews, and focus groups from three LTCFs in Western Canada. Descriptive statistics of survey data and thematic analysis of qualitative survey responses and focus groups were used to identify themes in staff experiences with Toch Sleepsense. Results: Staff valued the utility of Toch Sleepsense in providing alerts that support timely interventions and fall prevention. Staff further recognized the value of sensor devices in decreasing repetitive nighttime checks and providing vital sign monitoring. Toch Sleepsense data informed care planning and improved resident comfort. Inconsistent internet connectivity, sensor realignments, and limited training posed challenges to reliability. Conclusions: Sensor technologies like Toch Sleepsense show potential to improve safety, support staff workload management, and improve care practices. Sustained benefits require reliable technical infrastructure, comprehensive staff training, and strong leadership support. Full article

(This article belongs to the Special Issue Non-Intrusive Sensors for Human Activity Detection and Recognition)

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18 pages, 6358 KB

Open AccessArticle

A CMOS Voltage Reference with PTAT Current Using DIBL Compensation for Low Line Sensitivity

by Minji Jung and Youngwoo Ji

Sensors 2025, 25(21), 6794; https://doi.org/10.3390/s25216794 - 6 Nov 2025

Viewed by 401

Abstract

This paper presents a low-power CMOS voltage reference with low supply sensitivity, designed and verified in a 180 nm standard CMOS technology. A DIBL-based line-sensitivity (LS) compensation path is incorporated into the conventional PTAT generation circuit to simultaneously provide a reference voltage and [...] Read more.

This paper presents a low-power CMOS voltage reference with low supply sensitivity, designed and verified in a 180 nm standard CMOS technology. A DIBL-based line-sensitivity (LS) compensation path is incorporated into the conventional PTAT generation circuit to simultaneously provide a reference voltage and a bias current with improved LS. The proposed circuit achieves LS values of 0.01%/V for the voltage reference and 0.07%/V for the bias current reference over a supply voltage range of 1.4 V to 2 V. It generates a reference voltage of 538 mV and a PTAT current of 38 nA, consuming 68 nW. The simulated temperature coefficient is 58 ppm/

℃

from −40 °C to 130 °C, and the power supply rejection ratio is −59 dB at 100 Hz. Full article

(This article belongs to the Special Issue Sensors and Analog Front-End Circuits for Sensing Systems and High Sensitivity Measurements: 2nd Edition)

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14 pages, 2386 KB

Open AccessArticle

Introduction of RKKY-pMTJ-Based Ultrafast Magnetic Sensor Architecture and Magnetic Multilayer Optimization

by Jaehun Cho and June-Seo Kim

Sensors 2025, 25(21), 6793; https://doi.org/10.3390/s25216793 - 6 Nov 2025

Viewed by 470

Abstract

A state-of-the-art tunnel magnetoresistance (TMR) sensor architecture, which is based on the perpendicularly magnetized magnetic tunnel junction (pMTJ), is introduced and engineered for ultrafast, high thermal stability, and linearity for magnetic field detection. Limitations in high-frequency environments, stemming from insufficient thermal stability and [...] Read more.

A state-of-the-art tunnel magnetoresistance (TMR) sensor architecture, which is based on the perpendicularly magnetized magnetic tunnel junction (pMTJ), is introduced and engineered for ultrafast, high thermal stability, and linearity for magnetic field detection. Limitations in high-frequency environments, stemming from insufficient thermal stability and slow recovery times in conventional TMR sensors, are overcome by this approach. The standard MRAM structure is modified, and the Ruderman–Kittel–Kasuya–Yosida (RKKY) interaction is employed to give a strong, internal restoring torque to the storage layer magnetization. Sensor linearity is also ensured by this RKKY mechanism, and rapid relaxation to the initial spin state is observed when an external field is removed. The structural and magnetic properties of the multilayer stack are experimentally demonstrated. Robust synthetic antiferromagnetic (SAF) coupling is confirmed by using polar MOKE spectroscopy with an optimal Ru insertion layer thickness (0.6 nm), which is essential for high thermal stability. Subsequently, an ultrafast response of this TMR sensor architecture is probed by micromagnetic simulations. The storage layer magnetization rapidly recovers to the SAF state within an ultrashort time of 5.78 to 5.99 ns. This sub-6 ns recovery time scale suggests potential operation into the hundreds of MHz range. Full article

(This article belongs to the Section State-of-the-Art Sensors Technologies)

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31 pages, 9707 KB

Open AccessArticle

A Digitization Framework for Belt Rotation Monitoring in Pipe Conveyor Applications

by Leonardo dos Santos e Santos, Paulo Roberto Campos Flexa Ribeiro Filho and Emanuel Negrão Macêdo

Sensors 2025, 25(21), 6792; https://doi.org/10.3390/s25216792 - 6 Nov 2025

Viewed by 530

Abstract

Pipe conveyors provide an environmentally friendly alternative to open-troughed bulk solids conveyance, particularly for long or complex routing applications. However, the sustainability of this technology is compromised by unstable operations. Complex routing, operational variations, and environmental factors create uneven contact forces, triggering belt [...] Read more.

Pipe conveyors provide an environmentally friendly alternative to open-troughed bulk solids conveyance, particularly for long or complex routing applications. However, the sustainability of this technology is compromised by unstable operations. Complex routing, operational variations, and environmental factors create uneven contact forces, triggering belt rotation. This is a critical failure mode that requires continuous monitoring throughout the conveyor’s lifecycle. Insufficient failure data represents a typical challenge for this application. This study hypothesized technological principles that constitute the minimum requirements for enabling the scaling of industrial applications of belt rotation monitoring. Enabling technologies were adopted to foster innovation, and a physical prototype was implemented to address data scarcity for this failure mode. Using a controller-responder wireless network of ESP32 Industrial Internet of Things devices, we developed a belt-independent measurement system with multiparameter capability. Key criteria for detecting unsafe operational states and a criticality-based approach for determining optimal measuring unit quantities were established. The measurement results demonstrated suitable precision for digitization objectives: overlap angle (3.3107° ± 16.7562°), pipe diameter (+13.3850 ± 7.2114 mm), and overlap length (−26.2750 ± 25.1536 mm), based on 307 samples with a latency of 350.1303 ms. The framework demonstrates potential for industrial deployment with acceptable performance for real-time monitoring. Full article

(This article belongs to the Section Internet of Things)

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35 pages, 18912 KB

Open AccessReview

Precision Nanometrology: Laser Interferometer, Grating Interferometer and Time Grating Sensor

by Can Cui and Xinghui Li

Sensors 2025, 25(21), 6791; https://doi.org/10.3390/s25216791 - 6 Nov 2025

Viewed by 826

Abstract

Displacement metrology with nanometer-level precision over macroscopic ranges is a key foundation for modern science and engineering. This review provides a comparative overview of Precision Nanometrology, covering measurement ranges from micrometers to meters and accuracies between 0.1 nm and 100 nm. Three main [...] Read more.

Displacement metrology with nanometer-level precision over macroscopic ranges is a key foundation for modern science and engineering. This review provides a comparative overview of Precision Nanometrology, covering measurement ranges from micrometers to meters and accuracies between 0.1 nm and 100 nm. Three main technologies are discussed: the Laser Interferometer (LI), the Grating Interferometer (GI), and the Time Grating Sensor (TGS). The LI is widely regarded as the traceable benchmark for highest resolution; the GI has been developed into a compact and stable solution based on diffraction gratings; and the TGS has emerged as a new approach that converts spatial displacement into the time domain, offering strong resilience to environmental fluctuations. For each technique, the principles, recent progress, and representative systems from the past two decades are reviewed. Particular attention is given to the trade-offs between resolution, robustness, and scalability, which are decisive for practical deployment. The review concludes with a comparative analysis of performance indicators and a perspective on future directions, highlighting hybrid architectures and application-driven requirements in precision manufacturing and advanced instrumentation. Full article

(This article belongs to the Section Physical Sensors)

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20 pages, 2328 KB

Open AccessArticle

FedPSFV: Personalized Federated Learning via Prototype Sharing for Finger Vein Recognition

by Haoyan Xu, Yuyang Guo, Yunzan Qu, Jian Guo and Hengyi Ren

Sensors 2025, 25(21), 6790; https://doi.org/10.3390/s25216790 - 6 Nov 2025

Viewed by 452

Abstract

Finger vein recognition algorithms based on deep learning techniques are widely used in many fields. However, the training of finger vein recognition models is hindered by privacy issues and the scarcity of public datasets. Although applying federated learning techniques to finger vein recognition [...] Read more.

Finger vein recognition algorithms based on deep learning techniques are widely used in many fields. However, the training of finger vein recognition models is hindered by privacy issues and the scarcity of public datasets. Although applying federated learning techniques to finger vein recognition can effectively address privacy concerns, data heterogeneity across clients limits the performance of the models, especially on small datasets. To address these problems, in this paper, we propose a new federated finger vein recognition algorithm (FedPSFV). The algorithm is based on the federated learning framework, which increases the interclass distance of each dataset by sharing the prototypes among clients to solve the data heterogeneity problem. The algorithm also integrates and improves the margin-based loss function, which advances the feature differentiation ability of the model. Comparative experiments based on six public datasets (SDUMLA, MMCBNU, USM, UTFVP, VERA, and NUPT) show that FedPSFV has better accuracy and generalizability; the TAR@FAR = 0.01 is improved by 5.00–11.25%, and the EER is reduced by 81.48–90.22% compared to the existing methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Edge Computing in IoT-Based Applications)

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28 pages, 5155 KB

Open AccessArticle

Efficient Human Posture Recognition and Assessment in Visual Sensor Systems: An Experimental Study

by Lei Lei, Haonan Zhang, Qi Zhang, Weihua Wu, Weijia Han and Runzi Liu

Sensors 2025, 25(21), 6789; https://doi.org/10.3390/s25216789 - 6 Nov 2025

Viewed by 552

Abstract

Currently, recognition and assessment of human posture have become significant topics of interest, particularly through the use of visual sensor systems. These approaches can effectively address the drawbacks associated with traditional manual assessments, which include fatigue, variations in experience, and inconsistent judgment criteria. [...] Read more.

Currently, recognition and assessment of human posture have become significant topics of interest, particularly through the use of visual sensor systems. These approaches can effectively address the drawbacks associated with traditional manual assessments, which include fatigue, variations in experience, and inconsistent judgment criteria. However, systems based on visual sensors encounter substantial implementation challenges when a large number of such sensors are used. To address these issues, we propose a human posture recognition and assessment system architecture, which comprises four distinct subsystems. Specifically, these subsystems include a Visual Sensor Subsystem (VSS), a Posture Assessment Subsystem (PAS), a Control-Display Subsystem, and a Storage Management Subsystem. Through the cooperation of subsystems, the architecture has achieved support for parallel data processing. Furthermore, the proposed architecture has been implemented by building an experimental testbed, which effectively verifies the rationality and feasibility of this architecture. In the experiments, the proposed architecture was evaluated by using pull-up and push-up exercises. The results demonstrate that the proposed architecture achieves an overall accuracy exceeding 96%, while exhibiting excellent real-time performance and scalability in different assessment scenarios. Full article

(This article belongs to the Special Issue Technologies, Challenges, Applications, and Emerging Trends in Sensor-Enabled Embedded and Ubiquitous Computing)

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17 pages, 4583 KB

Open AccessArticle

VR for Situational Awareness in Real-Time Orchard Architecture Assessment

by Andrew K. Chesang and Daniel Dooyum Uyeh

Sensors 2025, 25(21), 6788; https://doi.org/10.3390/s25216788 - 6 Nov 2025

Viewed by 459

Abstract

Teleoperation in agricultural environments requires enhanced situational awareness for effective architectural scouting and decision-making for orchard management applications. The dynamic complexity of orchard structures presents challenges for remote visualization during architectural scouting operations. This study presents an adaptive streaming and rendering pipeline for [...] Read more.

Teleoperation in agricultural environments requires enhanced situational awareness for effective architectural scouting and decision-making for orchard management applications. The dynamic complexity of orchard structures presents challenges for remote visualization during architectural scouting operations. This study presents an adaptive streaming and rendering pipeline for real-time point cloud visualization in Virtual Reality (VR) teleoperation systems. The proposed method integrates selective streaming that localizes teleoperators within live maps, an efficient point cloud parser for Unity Engine, and an adaptive Level-of-Detail rendering system utilizing dynamically scaled and smoothed polygons. The implementation incorporates pseudo-coloring through LiDAR reflectivity fields to enhance the distinction between materials and geometry. The pipeline was evaluated using datasets containing LiDAR point cloud scans of orchard environments captured during spring and summer seasons, with testing conducted on both standalone and PC-tethered VR configurations. Performance analysis demonstrated improvements of 10.2–19.4% in runtime performance compared to existing methods, with a framerate enhancement of up to 112% achieved through selectively streamed representations. Qualitative assessment confirms the method’s capability to maintain visual continuity at close proximity while preserving the geometric features discernible for architectural scouting operations. The results establish the viability of VR-based teleoperation for precision agriculture applications, while demonstrating the critical relationship between Quality-of-Service parameters and operator Quality of Experience in remote environmental perception. Full article

(This article belongs to the Special Issue Sensing Technology and Computer Vision for Precision Agriculture and Smart Farming)

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20 pages, 3385 KB

Open AccessArticle

Extended State Observer-Based Chattering Free Terminal Sliding-Mode Control of Hydraulic Manipulators

by Han Gao, Jingran Ma, Yanjun Liu and Gang Xue

Sensors 2025, 25(21), 6787; https://doi.org/10.3390/s25216787 - 6 Nov 2025

Viewed by 334

Abstract

High-performance tracking control for the hydraulic manipulator should address the challenges of the uncertainties and unknowns associated with the electro-hydraulic servo system (EHSS). This paper presents an extended state observer-based chattering-free terminal sliding-mode (ESO-CFTSM) control scheme for hydraulic manipulators. A third-order integral chain [...] Read more.

High-performance tracking control for the hydraulic manipulator should address the challenges of the uncertainties and unknowns associated with the electro-hydraulic servo system (EHSS). This paper presents an extended state observer-based chattering-free terminal sliding-mode (ESO-CFTSM) control scheme for hydraulic manipulators. A third-order integral chain model is developed to characterize the system dynamics, where uncertainties and unknowns are considered as disturbances and estimated by the ESO. Meanwhile, a full-order TSM manifold is designed to stabilize the closed-loop system in finite-time. For this proposed scheme, the feedforward compensation of disturbances is introduced in the equivalent control law. Furthermore, the composite reaching law and a low-pass filter are used to realize the chattering-free control. The singularity is avoided because there are no derivatives of terms with fractional powers in the control law. The stability of the overall system is proved by Lyapunov technique. The simulations using the physical model of a hydraulic manipulator with coupled dynamics show the effectiveness of the proposed scheme for trajectory tracking problems. Simulation results indicate that the proposed ESO-CFTSM can achieve superior performance without being affected by lumped disturbances. Full article

(This article belongs to the Section Industrial Sensors)

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20 pages, 17234 KB

Open AccessArticle

Deep Lidar-Guided Image Deblurring

by Ziyao Yi, Diego Valsesia, Tiziano Bianchi and Enrico Magli

Sensors 2025, 25(21), 6786; https://doi.org/10.3390/s25216786 - 6 Nov 2025

Viewed by 585

Abstract

The rise in portable Lidar instruments enables new opportunities for depth-assisted image processing. In this paper, we study whether the depth information provided by mobile Lidar sensors present in recent smartphones is useful for the task of image deblurring and how to integrate [...] Read more.

The rise in portable Lidar instruments enables new opportunities for depth-assisted image processing. In this paper, we study whether the depth information provided by mobile Lidar sensors present in recent smartphones is useful for the task of image deblurring and how to integrate it with a general approach that transforms any state-of-the-art neural deblurring model into a depth-aware one. To achieve this, we developed a continual learning strategy integrating adapters into U-shaped encoder–decoder models that efficiently preprocess depth information to modulate image features with depth features. We conducted experiments on datasets with real-world depth data captured by a smartphone Lidar. The results show that our method consistently improves performance across multiple state-of-the-art deblurring baselines. Our approach achieves PSNR gains of up to 2.1 dB with a modest increase in the number of parameters, which demonstrates that utilizing true depth information can significantly boost the effectiveness of deblurring algorithms with the encoder–decoder architecture. Full article

(This article belongs to the Section Sensing and Imaging)

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18 pages, 2518 KB

Open AccessArticle

An Efficient Vision Mamba–Transformer Hybrid Architecture for Abdominal Multi-Organ Image Segmentation

by Fang Lu, Jingyu Xu, Qinxiu Sun and Qiong Lou

Sensors 2025, 25(21), 6785; https://doi.org/10.3390/s25216785 - 6 Nov 2025

Viewed by 887

Abstract

Accurate abdominal multi-organ segmentation is essential for disease diagnosis and treatment planning. Although numerous deep-learning models have been proposed, current methods still struggle to balance segmentation accuracy with computational efficiency, particularly for images exhibiting inhomogeneous intensity distributions and complex anatomical structures. To address [...] Read more.

Accurate abdominal multi-organ segmentation is essential for disease diagnosis and treatment planning. Although numerous deep-learning models have been proposed, current methods still struggle to balance segmentation accuracy with computational efficiency, particularly for images exhibiting inhomogeneous intensity distributions and complex anatomical structures. To address these challenges, we present a hybrid framework that integrates an Efficient Vision Mamba (EViM) module into a Transformer-based encoder. The EViM module leverages hidden-state mixer-based state-space duality to enable efficient global context modelling and channel-wise interactions. In addition, a weighted combination of cross-entropy and Jaccard loss is employed to improve boundary delineation. Experimental results on the Synapse dataset demonstrate that the proposed model achieves an average Dice score of 82.67% and an HD95 of 16.36 mm, outperforming current state-of-the-art methods. Further validation on the ACDC cardiac MR dataset confirms the generalizability of our approach across imaging modalities. The results indicate that the proposed framework achieves high segmentation accuracy while effectively integrating global and local information, offering a practical and robust solution for clinical abdominal multi-organ segmentation. Full article

(This article belongs to the Section Biomedical Sensors)

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23 pages, 5372 KB

Open AccessArticle

Research and Experimental Testing of a Remotely Controlled Ankle Rehabilitation Exoskeleton Prototype

by Assylbek Ozhiken, Gani Sergazin, Kassymbek Ozhikenov, Haohan Wang, Nursultan Zhetenbayev, Gulzhamal Tursunbayeva, Asset Nurmangaliyev and Arman Uzbekbayev

Sensors 2025, 25(21), 6784; https://doi.org/10.3390/s25216784 - 6 Nov 2025

Viewed by 1065

Abstract

Today, there is a high demand for remote rehabilitation using mobile robotic complexes all over the world. They offer a wide range of options for convenient and effective therapy at home to patients and the elderly, especially those bedridden after musculoskeletal injuries. In [...] Read more.

Today, there is a high demand for remote rehabilitation using mobile robotic complexes all over the world. They offer a wide range of options for convenient and effective therapy at home to patients and the elderly, especially those bedridden after musculoskeletal injuries. In this case, modern approaches to the development of exoskeletons for the rehabilitation of the lower extremities are especially relevant for the effective restoration of lost motor functions. Taking into account the advantages and features of robotic rehabilitation, this work is devoted to the development of a prototype exoskeleton for the ankle joint and experimental studies of the remote control module. The proposed new exoskeleton prototype design was integrated with a mobile wireless communication platform, allowing remote control of the position of the exoskeleton foot using a remote control device. As a result of functional testing, the root mean square error (RMSE) was 23.9° for dorsiflexion/plantarflexion movements and 12.8° for inversion and eversion movements, as well as an average signal transmission delay of about 100 ms and packet loss of 0.6%. These results reflect the technical feasibility of remote control at a distance of up to 10 m. The developed system is mobile, autonomous, and easy to use, which confirms its suitability as a laboratory platform for functional verification and testing of module consistency. Full article

(This article belongs to the Section Sensors and Robotics)

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21 pages, 1020 KB

Open AccessArticle

Robust 3D Skeletal Joint Fall Detection in Occluded and Rotated Views Using Data Augmentation and Inference–Time Aggregation

by Maryem Zobi, Lorenzo Bolzani, Youness Tabii and Rachid Oulad Haj Thami

Sensors 2025, 25(21), 6783; https://doi.org/10.3390/s25216783 - 6 Nov 2025

Viewed by 754

Abstract

Fall detection systems are a critical application of human pose estimation, frequently struggle with achieving real-world robustness due to their reliance on domain-specific datasets and a limited capacity for generalization to novel conditions. Models trained on controlled, canonical camera views often fail when [...] Read more.

Fall detection systems are a critical application of human pose estimation, frequently struggle with achieving real-world robustness due to their reliance on domain-specific datasets and a limited capacity for generalization to novel conditions. Models trained on controlled, canonical camera views often fail when subjects are viewed from new perspectives or are partially occluded, resulting in missed detections or false positives. This study tackles these limitations by proposing the Viewpoint Invariant Robust Aggregation Graph Convolutional Network (VIRA-GCN), an adaptation of the Richly Activated GCN for fall detection. The VIRA-GCN introduces a novel dual-strategy solution: a synthetic viewpoint generation process to augment training data and an efficient inference-time aggregation method to form consensus-based predictions. We demonstrate that augmenting the Le2i dataset with simulated rotations and occlusions allows a standard pose estimation model to achieve a significant increase in its fall detection capabilities. The VIRA-GCN achieved 99.81% accuracy on the Le2i dataset, confirming its enhanced robustness. Furthermore, the model is suitable for low-resource deployment, utilizing only 4.06 M parameters and achieving a real-time inference latency of 7.50 ms. This work presents a practical and efficient solution for developing a single-camera fall detection system robust to viewpoint variations, and introduces a reusable mapping function to convert Kinect data to the MMPose format, ensuring consistent comparison with state-of-the-art models. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors for Telehealth Monitoring: Movement Tracking and Analysis)

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23 pages, 5476 KB

Open AccessArticle

SMA-Driven Assistive Hand for Rehabilitation Therapy

by Grace Mayhead, Megan Rook, Rosario Turner, Owen Walker, Nabila Naz and Soumya K. Manna

Sensors 2025, 25(21), 6782; https://doi.org/10.3390/s25216782 - 5 Nov 2025

Viewed by 832

Abstract

Home-based rehabilitation supports neuromuscular patients while minimising the need for extensive clinical supervision. Due to a growing number of stroke survivors, this approach appears to be more practical for patients across diverse demographics. Although existing hardware-based assistive devices are pretty common, they have [...] Read more.

Home-based rehabilitation supports neuromuscular patients while minimising the need for extensive clinical supervision. Due to a growing number of stroke survivors, this approach appears to be more practical for patients across diverse demographics. Although existing hardware-based assistive devices are pretty common, they have limitations in terms of usability, wearability, and safety, as well as other technical constraints such as bulkiness and torque-to-weight ratios. To overcome these issues, soft actuator–based assistance prioritises user safety and ergonomics, as it is more wearable and lightweight, offering overall support while reducing the social stigma associated with disability. Among the existing soft actuation techniques and related materials, shape memory alloys (SMA) present a feasible option, offering current-controlled actuation and compatibility with integration into flexible textiles, thereby enhancing the wearability of the device. This paper presents a compact, SMA-driven assistive device designed to support natural motion, reduce muscle fatigue, and enable daily therapy. Embedded in a glove, the device allows mirrored control, where one hand’s movement assists the other, using flex sensors for feedback. The functionality of the electromyography (EMG) sensor is also used to evaluate the activation of the SMA wire; however, it is not employed for detecting individual finger motions in the assistive hand. Polyurethane foam insulation minimises thermal effects while maintaining lightweight wearability. Experimental results demonstrate a reduction in actuation time at higher voltages and the effective lifting of light to moderate weights. The device shows strong potential for affordable, home-based rehabilitation and everyday assistance. Full article

(This article belongs to the Special Issue Sensing and AI: Advancements in Robotics and Autonomous Systems)

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18 pages, 7443 KB

Open AccessArticle

Generating Accurate Activity Patterns for Cattle Farm Management Using MCMC Simulation of Multiple-Sensor Data System

by Yukie Hashimoto, Thi Thi Zin, Pyke Tin, Ikuo Kobayashi and Hiromitsu Hama

Sensors 2025, 25(21), 6781; https://doi.org/10.3390/s25216781 - 5 Nov 2025

Viewed by 557

Abstract

This paper presents a novel Markov Chain Monte Carlo (MCMC) simulation model for analyzing multi-sensor data to enhance cattle farm management. As Precision Livestock Farming (PLF) systems become more widespread, leveraging data from technologies like 3D acceleration, pneumatic, and proximity sensors is crucial [...] Read more.

This paper presents a novel Markov Chain Monte Carlo (MCMC) simulation model for analyzing multi-sensor data to enhance cattle farm management. As Precision Livestock Farming (PLF) systems become more widespread, leveraging data from technologies like 3D acceleration, pneumatic, and proximity sensors is crucial for deriving actionable insights into animal behavior. Our research addresses this need by demonstrating how MCMC can be used to accurately model and predict complex cattle activity patterns. We investigate the direct impact of these insights on optimizing key farm management areas, including feed allocation, early disease detection, and labor scheduling. Using a combination of controlled monthly experiments and the analysis of uncontrolled, real-world data, we validate our proposed approach. The results confirm that our MCMC simulation effectively processes diverse sensor inputs to generate reliable and detailed behavioral patterns. We find that this data-driven methodology provides significant advantages for developing informed management strategies, leading to improvements in the overall efficiency, productivity, and profitability of cattle operations. This work underscores the potential of using advanced statistical models like MCMC to transform multi-sensor data into tangible improvements for modern agriculture. Full article

(This article belongs to the Special Issue Sensors and Data-Driven Precision Agriculture—Second Edition)

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19 pages, 5481 KB

Open AccessArticle

Cnidaria-Inspired Morphing Mechanism for Underwater Robot: A Soft Tectonics Approach

by Yin Yu

Sensors 2025, 25(21), 6780; https://doi.org/10.3390/s25216780 - 5 Nov 2025

Viewed by 731

Abstract

Soft robots demonstrate great potential for underwater exploration, particularly in tasks such as locomotion and biological sampling in fragile marine habitats. However, developing new forms of interaction with underwater life remains a challenge due to inadequate soft mechanisms for studying the behavior of [...] Read more.

Soft robots demonstrate great potential for underwater exploration, particularly in tasks such as locomotion and biological sampling in fragile marine habitats. However, developing new forms of interaction with underwater life remains a challenge due to inadequate soft mechanisms for studying the behavior of marine invertebrates. We present a 7-cm in diameter anemone robot (“Soromone”) capable of performing biological sea anemones’ wiggling behavior under the water. Inspired by the body forms of adult cnidaria, we developed a morphing mechanism that serves as both structure and actuator for the Soromone’s behavior using a soft tectonics approach—a multistep, multiscale, heterogeneous soft material fabrication technique. As an actuator, the morphing mechanism can precisely control the Soromone via a fluid system; as a structure, it can reinstate the Soromone’s original shape by incorporating various degrees of stiffness or softness into a single piece of material during fabrication. Our study demonstrates the advantages of applying a Soromone under water, including increasing water flow for enhanced nutrient uptake, waste removal, and gas exchange. This cnidaria-inspired soft robot could potentially be adapted for interaction with coral reef ecosystems by providing a safe environment for diverse species. Future soft robotics design paradigms based on a soft tectonics approach could expand the variability and applicability of soft robots for underwater exploration and habitation. Full article

(This article belongs to the Special Issue Soft Sensors and Sensing Techniques)

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21 pages, 3938 KB

Open AccessArticle

Dynamic Incidence Angle Effects of Non-Spherical Raindrops on Rain Attenuation and Scattering for Millimeter-Wave Fuzes

by Bing Yang, Kaiwei Wu, Yanbin Liang, Shijun Hao and Zhonghua Huang

Sensors 2025, 25(21), 6779; https://doi.org/10.3390/s25216779 - 5 Nov 2025

Viewed by 446

Abstract

The dynamic variation of the incidence angle between the millimeter-wave (MMW) fuzes and non-spherical raindrops significantly affects detection performance. To address this issue, the influence of incidence angle on attenuation coefficient, volume reflectivity, and the signal-to-clutter-plus-noise ratio (SCNR) is systematically analyzed by employing [...] Read more.

The dynamic variation of the incidence angle between the millimeter-wave (MMW) fuzes and non-spherical raindrops significantly affects detection performance. To address this issue, the influence of incidence angle on attenuation coefficient, volume reflectivity, and the signal-to-clutter-plus-noise ratio (SCNR) is systematically analyzed by employing the realistic raindrop morphology described by the Beard and Chuang (BC) model and the invariant imbedding (IIM) T-matrix method. By integrating worst-case analysis, the critical incidence angle corresponding to the most severe performance degradation is identified, and the corresponding attenuation coefficient, volume reflectivity, and SCNR values are reconstructed. Numerical simulations demonstrate that for the BC model, the most severe impact on MMW signal propagation occurs at an incidence angle of 180°. Under this condition, the reconstructed attenuation coefficient and volume reflectivity increase by 45.88% and 28.27%, respectively, while the SCNR decreases by 27.35% at 60 GHz operating frequency and 100 mm/h rainfall rate, compared to the spherical raindrop model. This study provides a theoretical basis for calibrating design margins and optimizing anti-interference strategies for MMW fuzes operating in complex meteorological conditions. Full article

(This article belongs to the Section Electronic Sensors)

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